[feature] support kvcache with static graph (#209)

* [feature] support kvcache with static graph

* use workspace to optimize kvcache attention

---------

Co-authored-by: Haojie Wang <haojie0429@gmail.com>

examples/python/llama_kvcache_inference.py

@@ -0,0 +1,145 @@
+import os
+from pyinfinitensor.onnx import OnnxStub, backend
+import numpy as np
+import onnx
+import torch
+from transformers import LlamaModel, LlamaForCausalLM
+from tqdm import tqdm
+import onnx_graphsurgeon as gs
+from onnxsim import simplify
+import argparse
+
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--batchsize', dest='batchsize', type=int, default=1)
+parser.add_argument('--layer', dest='n_layers', type=int, default=2)
+parser.add_argument('--iter', dest='n_iter', type=int, default=1)
+parser.add_argument('--n_max_length', dest='n_max_length', type=int, default=1024)
+parser.add_argument('--pretrained_llama_path', dest='pretrained_llama_path', type=str, 
+                    default="/data0/shared/data/public/opensource_models/meta-llama/Llama-2-7b-hf/")
+parser.add_argument('--onnx_model_path', dest='onnx_model_path', type=str, 
+                    default="/data1/shared/llama")
+args = parser.parse_args()
+
+ONNX_MODEL_PATH = "{}/llama_bs{}_layer{}.onnx".format(args.onnx_model_path, args.batchsize, args.n_layers)
+ONNX_WEIGHT_PATH = "./llama_bs{}_layer{}.pb".format(args.batchsize, args.n_layers)
+
+def export_onnx(model: LlamaModel, ONNX_MODEL_PATH):
+    param = torch.zeros(
+        (args.batchsize, 1024), dtype=torch.long)
+    logits = model(param, past_key_values=None)
+    param_kvcache = torch.zeros((args.batchsize, 1), dtype=torch.long)
+
+    torch.onnx.export(model, (param_kvcache, {"past_key_values": logits.past_key_values,
+                                              "position_ids": param_kvcache}), ONNX_MODEL_PATH, verbose=False,
+                      do_constant_folding=True,)
+    onnx_model = onnx.load(ONNX_MODEL_PATH)
+    print("simplifing onnx model")
+    onnx_model, check = simplify(onnx_model, skipped_optimizers=[
+                                 'eliminate_duplicate_initializer'])
+    assert check
+    
+    onnx.save(onnx_model, ONNX_MODEL_PATH, save_as_external_data=True, location=ONNX_WEIGHT_PATH)
+    print("simlifing finished.")
+
+
+@gs.Graph.register()
+def replace_with_attention(self, inputs, outputs, inputs_added, outputs_removed):
+    for inp in inputs:
+        inp.outputs.clear()   
+    for out in outputs:
+        out.inputs.clear()
+    for inp in inputs_added:
+        inputs.append(inp)
+    for out in outputs_removed:
+        out.inputs.clear()
+    return self.layer(op="AttentionKVCache", inputs=inputs, outputs=outputs)
+
+
+def replace_onnx_with_attention_op():
+    graph = gs.import_onnx(
+        onnx.load(ONNX_MODEL_PATH))
+    tmap = graph.tensors()
+    for i in range(args.n_layers):
+        inputs = [
+            tmap["onnx::Concat_" + str((i+1)*2)],
+            tmap["onnx::Concat_" + str((i+1)*2+1)],
+            tmap["/model/layers." + str(i) + "/self_attn/Add_output_0"],
+            tmap["/model/layers." + str(i) + "/self_attn/Add_1_output_0"],
+            tmap["/model/layers." + str(i) + "/self_attn/Transpose_2_output_0"]]
+        outputs = [
+            tmap["/model/layers." + str(i) + "/self_attn/MatMul_1_output_0"]]
+
+        inputs_added = [graph.inputs[1]]
+        outputs_removed = []
+
+        graph.replace_with_attention(
+            inputs, outputs, inputs_added, outputs_removed)
+        
+    graph.outputs = [tmap[graph.outputs[0].name]]
+    graph.cleanup(True).toposort()
+    onnx.save(gs.export_onnx(graph), ONNX_MODEL_PATH, save_as_external_data=True)
+
+
+if __name__ == "__main__":
+    kvcache_torch = None
+    torch_model = LlamaForCausalLM.from_pretrained(
+        args.pretrained_llama_path, num_hidden_layers=int(args.n_layers)).eval()
+    
+    n_heads = torch_model.config.num_attention_heads
+    n_dims = torch_model.config.hidden_size // n_heads
+    
+    if not os.path.exists(ONNX_MODEL_PATH):
+        print("exporting onnx graph")
+        export_onnx(torch_model, ONNX_MODEL_PATH)
+        replace_onnx_with_attention_op()
+    else:
+        print("will use exsiting onnx graph")
+
+    onnx_model = onnx.load(ONNX_MODEL_PATH)
+    stub = OnnxStub(onnx_model, backend.cuda_runtime())
+
+    count_wrong = 0
+    for i in tqdm(range(0, args.n_max_length)):
+        query = np.random.randint(
+            torch_model.config.vocab_size, size=(args.batchsize, 1), dtype=np.int32)
+        position_id = i*np.ones((args.batchsize, 1), dtype=np.int32)
+
+        ####################################
+        # pytorch
+        ####################################
+        outputs_torch = torch_model(
+            torch.tensor(query), past_key_values=kvcache_torch)
+        logit_torch = outputs_torch['logits']
+        kvcache_torch = outputs_torch['past_key_values']
+
+        ####################################
+        # infinitensor
+        ####################################
+        # copyin input
+        (list(stub.inputs.items()))[0][1].copyin_int64(
+            query.reshape(-1).tolist())
+        (list(stub.inputs.items()))[1][1].copyin_int64(
+            position_id.reshape(-1).tolist())
+
+        stub.run()
+
+        ####################################
+        # validation
+        ####################################
+        # copyout output
+        logits_it = np.array((list(stub.outputs.items()))
+                                [0][1].copyout_float())
+        
+        try:
+            np.testing.assert_allclose(
+                logit_torch[:, -1, :].detach().cpu().numpy().flatten(), logits_it, rtol=1e-3, atol=1e-3)
+        except Exception as e: 
+            try:
+                np.testing.assert_allclose(
+                    np.argmax(logit_torch[:, -1, :].detach().cpu().numpy().flatten()), np.argmax(logits_it), rtol=1e-3, atol=1e-3)
+            except:
+                count_wrong = count_wrong + 1
+
+    result = "{}/{} failed.".format(count_wrong, args.n_max_length)
+    print(result)
+    del stub

include/cuda/cuda_attention_kvcache.h

@@ -1,4 +1,5 @@
 #pragma once
+#include "core/common.h"
 #include <cstdio>
 
 struct AttentionKVCacheMetadata {
@@ -10,6 +11,7 @@ namespace infini {
 void attention_kvcache_kernel(float *input_k_cache, float *input_v_cache,
                               float *input_q, float *input_k, float *input_v,
                               int *position_id, float *output_matmul,
-                              const AttentionKVCacheMetadata &compMeta);
+                              const AttentionKVCacheMetadata &compMeta,
+                              float *output_O_temp, float *output_sum_temp);
 
 } // namespace infini

src/core/graph_handler.cc

@@ -334,7 +334,7 @@ Tensor GraphHandlerObj::attentionKVCache(Tensor input_k_cache,
             std::move(input_k_cache), std::move(input_v_cache),
             std::move(input_q), std::move(input_k), std::move(input_v),
             std::move(position_id), output_matmul);
-        return {output_matmul};
+        return output_matmul;
     } else {
         return g
             ->addOp<AttentionKVCacheObj>(

src/kernels/cuda/attention_kvcache.cc

@@ -21,7 +21,7 @@ class AttentionKVCacheCompute {
   public:
     void do_compute(Tensor input_k_cache, Tensor input_v_cache, Tensor input_q,
                     Tensor input_k, Tensor input_v, Tensor position_id,
-                    Tensor output_matmul) const {
+                    Tensor output_matmul, CudaPtr p_workspace) const {
         AttentionKVCacheMetadata metadata;
         initAttentionKVCacheMetadata(metadata, input_v_cache);
 
@@ -32,7 +32,8 @@ class AttentionKVCacheCompute {
                                  input_v->getRawDataPtr<float *>(),
                                  position_id->getRawDataPtr<int *>(),
                                  output_matmul->getRawDataPtr<float *>(),
-                                 metadata);
+                                 metadata, (float *)p_workspace,
+                                 (float *)(p_workspace + (1ll << 30)));
     }
 };
 
@@ -41,10 +42,14 @@ class AttentionKVCacheCuda : private AttentionKVCacheCompute,
     void compute(const Operator &_op,
                  const RuntimeObj *_context) const override {
         IT_ASSERT(_op->getDType() == DataType::Float32);
+
+        size_t workspaceSize = 2ll << 30;
+        auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
+        CudaPtr idxWsData = context->getWorkspace(workspaceSize);
         do_compute(_op->getInputs()[0], _op->getInputs()[1],
                    _op->getInputs()[2], _op->getInputs()[3],
                    _op->getInputs()[4], _op->getInputs()[5],
-                   _op->getOutputs()[0]);
+                   _op->getOutputs()[0], idxWsData);
     }
 };
 

src/kernels/cuda/attention_kvcache.cu

@@ -2,127 +2,168 @@
 #include "cuda/cuda_attention_kvcache.h"
 #define WARP_SIZE 32
 #define BLOCKSIZE WARP_SIZE
-#define SEQ_UNIT 64
+#define SEQ_UNIT 32
 
-__global__ void _attention_kvcache_kernel(float* input_k_cache,
+// ASSUME SEQ_LEN OF Q IS 1
+__global__ void _attention_kvcache_kernel_128_1(float* input_k_cache,
                                               float* input_v_cache, 
                                               float* input_q, 
                                               float* input_k, 
                                               float* input_v, 
                                               int* position_id,
-                                              float* output_matmul,
+                                              AttentionKVCacheMetadata compMeta,
-                                              AttentionKVCacheMetadata compMeta) {
+                                              float* output_O_temp,
+                                              float* output_sum_temp) {
+    int seq_length = position_id[0] + 1;
+    int stride = (seq_length + SEQ_UNIT - 1) / SEQ_UNIT;
+    if(blockIdx.y >= stride)
+        return;
+
     int lane_id = threadIdx.x % WARP_SIZE;
     int group_id = threadIdx.x / WARP_SIZE;
     int parallel_idx = blockIdx.x * (blockDim.x / WARP_SIZE) + group_id;
+    int idx_seq = blockIdx.y * SEQ_UNIT; 
 
     if(parallel_idx >= compMeta.dimSize[0] * compMeta.dimSize[1])
         return;
 
-    float ptr_V[SEQ_UNIT*2]; 
+    float ptr_V[SEQ_UNIT*4]; // V
-    float ptr_K[SEQ_UNIT*2]; 
+    float ptr_K[SEQ_UNIT*4]; // K
-    float ptr_Q[2]; 
+    float ptr_Q[4];          // Q
-    float ptr_P[SEQ_UNIT];
+    float ptr_P[SEQ_UNIT] = {0};
 
-    float ptr_O[2];
+    float ptr_O[4] = {0};
-    float ptr_max[1];
+    float ptr_sum[1] = {0};
-    float ptr_sum[1];
 
-    float ptr_max_last[1];
+    // readin Q
-    float ptr_sum_last[1];
+    (float4 &)ptr_Q[0] = (float4 &)input_q[(lane_id * 4) + (parallel_idx * 128)];
-    float ptr_O_last[2];
+    int common_idx = (lane_id * 4) + (parallel_idx * compMeta.stride[1]);
-
-    (float2 &)ptr_Q[0] = (float2 &)input_q[(lane_id * 2) + (parallel_idx * 64)];
-
-    int SEQ_LENGTH = position_id[0] + 1;
-
-    int common_idx = (lane_id * 2) + (parallel_idx * compMeta.stride[1]);
-
-
-    for (int idx_seq = 0; idx_seq < SEQ_LENGTH; idx_seq += SEQ_UNIT){ 
-        ptr_max_last[0] = ptr_max[0];
-        ptr_sum_last[0] = ptr_sum[0];
-        (float2 &)ptr_O_last[0] = (float2 &)ptr_O[0];
 
+    // Q*K
     #pragma unroll
-        for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < SEQ_LENGTH; idx_SEQ_UNIT ++) { 
+    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-            if(idx_SEQ_UNIT + idx_seq < SEQ_LENGTH - 1){                  
+        if(idx_SEQ_UNIT + idx_seq < seq_length - 1){                  
-                (float2 &)ptr_K[idx_SEQ_UNIT * 2] 
+            (float4 &)ptr_K[idx_SEQ_UNIT * 4] 
-                    = (float2 &) input_k_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])];
+                = (float4 &) input_k_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])];
         }
         else{
-                (float2 &)ptr_K[idx_SEQ_UNIT * 2] 
+            (float4 &)ptr_K[idx_SEQ_UNIT * 4] 
-                    = (float2 &) input_k[((lane_id * 2) + parallel_idx * compMeta.stride[2])];
+                = (float4 &) input_k[((lane_id * 4) + parallel_idx * compMeta.stride[2])];
-                (float2 &)input_k_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])] =
+            (float4 &)input_k_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])] =
-                    (float2 &)ptr_K[idx_SEQ_UNIT * 2];
+                (float4 &)ptr_K[idx_SEQ_UNIT * 4];
         }
-            ptr_K[idx_SEQ_UNIT * 2] = ptr_Q[0] * ptr_K[idx_SEQ_UNIT * 2];
-            ptr_K[idx_SEQ_UNIT * 2 + 1] = ptr_Q[1] * ptr_K[idx_SEQ_UNIT * 2 + 1];
 
+        
+        #pragma unroll
+        for (int i = 0; i < 4; i ++){
+            ptr_K[idx_SEQ_UNIT * 4 + i] = ptr_Q[i] * ptr_K[idx_SEQ_UNIT * 4 + i];
             #pragma unroll
             for (int offset = 16; offset > 0; offset /= 2) {
-                ptr_K[idx_SEQ_UNIT * 2] += __shfl_down_sync(0xffffffff, ptr_K[idx_SEQ_UNIT * 2], offset);
+                ptr_K[idx_SEQ_UNIT * 4 + i] += __shfl_down_sync(0xffffffff, ptr_K[idx_SEQ_UNIT * 4 + i], offset);
             }
-            ptr_P[idx_SEQ_UNIT] = ptr_K[idx_SEQ_UNIT * 2];
+            ptr_P[idx_SEQ_UNIT] += ptr_K[idx_SEQ_UNIT * 4 + i];
-            #pragma unroll
-            for (int offset = 16; offset > 0; offset /= 2){
-                ptr_K[((idx_SEQ_UNIT * 2) + 1)] += __shfl_down_sync(0xffffffff, ptr_K[((idx_SEQ_UNIT * 2) + 1)], offset);
         }
-            ptr_P[idx_SEQ_UNIT] += ptr_K[((idx_SEQ_UNIT * 2) + 1)];
     }
 
+    // div sqrt(d)
     #pragma unroll
-        for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < SEQ_LENGTH; idx_SEQ_UNIT ++) { 
+    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
         ptr_P[idx_SEQ_UNIT] = __shfl_sync(0xffffffff, ptr_P[idx_SEQ_UNIT], 0); 
-            ptr_P[idx_SEQ_UNIT] /= 8;
+        ptr_P[idx_SEQ_UNIT] /= sqrt(128.0);
-            ptr_max[0] = (idx_SEQ_UNIT == 0) ? ptr_P[0] : max(ptr_max[0], ptr_P[idx_SEQ_UNIT]);
     }
-        ptr_max[0] = (idx_seq == 0) ? ptr_max[0] : max(ptr_max[0], ptr_max_last[0]);
 
-        ptr_sum[0] = 0;
+    // softmax
     #pragma unroll
-        for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < SEQ_LENGTH; idx_SEQ_UNIT ++) { 
+    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-            ptr_P[idx_SEQ_UNIT] = expf(ptr_P[idx_SEQ_UNIT] - ptr_max[0]);
+        ptr_P[idx_SEQ_UNIT] = expf(ptr_P[idx_SEQ_UNIT]);
         ptr_sum[0] += ptr_P[idx_SEQ_UNIT];
     }
-        ptr_sum[0] = (idx_seq == 0) ? ptr_sum[0] : expf(ptr_max_last[0] - ptr_max[0]) * ptr_sum_last[0] + ptr_sum[0]; 
 
-        ptr_O[0] = 0;
+    // * V
-        ptr_O[1] = 0;
     #pragma unroll
-        for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < SEQ_LENGTH; idx_SEQ_UNIT ++) { 
+    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-            if(idx_SEQ_UNIT + idx_seq < SEQ_LENGTH - 1){                  
+        if(idx_SEQ_UNIT + idx_seq < seq_length - 1){                  
-                (float2 &)ptr_V[idx_SEQ_UNIT * 2] 
+            (float4 &)ptr_V[idx_SEQ_UNIT * 4] 
-                    = (float2 &) input_v_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])];
+                = (float4 &) input_v_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])];
         }
         else{
-                (float2 &)ptr_V[idx_SEQ_UNIT * 2] 
+            (float4 &)ptr_V[idx_SEQ_UNIT * 4] 
-                    = (float2 &) input_v[((lane_id * 2) + parallel_idx * compMeta.stride[2])];
+                = (float4 &) input_v[((lane_id * 4) + parallel_idx * compMeta.stride[2])];
-                (float2 &)input_v_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])] =
+            (float4 &)input_v_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])]
-                    (float2 &)ptr_V[idx_SEQ_UNIT * 2];
+                = (float4 &)ptr_V[idx_SEQ_UNIT * 4];
         }
 
-            ptr_P[idx_SEQ_UNIT] /= ptr_sum[0];
+        #pragma unroll
+        for (int i = 0; i < 4; i ++)
+            ptr_O[i] = fmaf(ptr_P[idx_SEQ_UNIT], ptr_V[(idx_SEQ_UNIT * 4 + i)], ptr_O[i]);
+    }
 
-            ptr_O[0] = fmaf(ptr_P[idx_SEQ_UNIT],  ptr_V[(idx_SEQ_UNIT * 2)], ptr_O[0]);
+    #pragma unroll
-            ptr_O[1] = fmaf(ptr_P[idx_SEQ_UNIT],  ptr_V[(idx_SEQ_UNIT * 2) + 1], ptr_O[1]);
+    for (int i = 0; i < 4; i ++)
+        ptr_O[i] /= ptr_sum[0];
+
+    (float4 &)output_O_temp[(lane_id * 4) + (blockIdx.y * compMeta.dimSize[3]) + (parallel_idx * compMeta.dimSize[3] * stride)] = (float4 &)ptr_O[0];
+    if(threadIdx.x == 0){
+        output_sum_temp[blockIdx.y + parallel_idx * stride] = ptr_sum[0];
     }
-        ptr_O[0] = (idx_seq == 0) ? ptr_O[0] : ptr_O[0] + ptr_O_last[0]  * expf(ptr_max_last[0] - ptr_max[0]) * ptr_sum_last[0] / ptr_sum[0];  
+
-        ptr_O[1] = (idx_seq == 0) ? ptr_O[1] : ptr_O[1] + ptr_O_last[1]  * expf(ptr_max_last[0] - ptr_max[0]) * ptr_sum_last[0] / ptr_sum[0]; 
-    }
-    (float2 &)output_matmul[(lane_id * 2) + (parallel_idx * compMeta.dimSize[3])] = (float2 &)ptr_O[0];
 }
 
+__global__ void _attention_kvcache_kernel_128_2(int* position_id,
+                                              float* output_matmul,
+                                              AttentionKVCacheMetadata compMeta,
+                                              float* output_O_temp,
+                                              float* output_sum_temp) {
+    int lane_id = threadIdx.x % WARP_SIZE;
+    int group_id = threadIdx.x / WARP_SIZE;
+    int parallel_idx = blockIdx.x * (blockDim.x / WARP_SIZE) + group_id;
+
+    float ptr_O[4] = {0};
+    float ptr_O_sum[4] = {0};
+    float ptr_sum = 0;
+    float ptr_sum_temp;
+    int size = (position_id[0] + SEQ_UNIT) / SEQ_UNIT;
+
+    #pragma unroll
+    for(int i = 0; i < size; i ++){
+        (float4 &)ptr_O[0] 
+            = (float4 &)output_O_temp[(lane_id * 4) + (i * compMeta.dimSize[3]) + parallel_idx * compMeta.dimSize[3] * size];
+        ptr_sum_temp = output_sum_temp[i + parallel_idx * size];
+
+        #pragma unroll
+        for(int k = 0; k < 4; k ++)
+            ptr_O_sum[k] += ptr_O[k] * ptr_sum_temp;
+        ptr_sum += ptr_sum_temp;
+    }
+
+    #pragma unroll
+    for(int k = 0; k < 4; k ++)
+        ptr_O_sum[k] = ptr_O_sum[k] / ptr_sum; 
+
+    (float4 &)output_matmul[(lane_id * 4) + (parallel_idx * compMeta.dimSize[3])] = (float4 &)ptr_O_sum[0];
+
+}
+
+
 namespace infini {
-void attention_kvcache_kernel(float *input_k_cache, float *input_v_cache, float *input_q, float *input_k,
+void attention_kvcache_kernel(float *input_k_cache, float *input_v_cache, 
+                          float *input_q, float *input_k,
                           float *input_v, int *position_id, float *output_matmul, 
-                          const AttentionKVCacheMetadata &compMeta) {
+                          const AttentionKVCacheMetadata &compMeta,
-    IT_ASSERT(compMeta.dimSize[3] == 64);
+                          float *output_O_temp, float *output_sum_temp) {
-    dim3 gridDim(compMeta.dimSize[0]*compMeta.dimSize[1]/(BLOCKSIZE/WARP_SIZE), 1);
+    IT_ASSERT(compMeta.dimSize[3] == 128);
+
+    int gridsize_y = (compMeta.dimSize[2] - 1 + SEQ_UNIT) / SEQ_UNIT;
+    dim3 gridDim(compMeta.dimSize[0]*compMeta.dimSize[1]/(BLOCKSIZE/WARP_SIZE), gridsize_y);
     dim3 blockDim(BLOCKSIZE, 1);
 
-    _attention_kvcache_kernel<<<gridDim, blockDim>>>(
+    assert(compMeta.dimSize[3] == 128);
-        input_k_cache, input_v_cache, input_q, input_k, input_v, position_id, output_matmul, compMeta);
+    _attention_kvcache_kernel_128_1<<<gridDim, blockDim>>>(
+        input_k_cache, input_v_cache, input_q, input_k, input_v, position_id, 
+        compMeta, output_O_temp, output_sum_temp);
+    _attention_kvcache_kernel_128_2<<<compMeta.dimSize[0]*compMeta.dimSize[1]/(BLOCKSIZE/WARP_SIZE), WARP_SIZE>>>(
+        position_id, output_matmul, compMeta, output_O_temp, output_sum_temp);
+    
 }
 
 } // namespace infini

test/kernels/cuda/test_cuda_attention.cc

@@ -14,11 +14,11 @@ TEST(AttentionKVCache, Cuda) {
 
     auto cudaRuntime = make_ref<CudaRuntimeObj>();
     Graph gCuda = make_ref<GraphObj>(cudaRuntime);
-    auto input_k_cache_d = gCuda->addTensor({1, 1, 1, 64}, DataType::Float32);
+    auto input_k_cache_d = gCuda->addTensor({1, 1, 1, 128}, DataType::Float32);
-    auto input_v_cache_d = gCuda->addTensor({1, 1, 1, 64}, DataType::Float32);
+    auto input_v_cache_d = gCuda->addTensor({1, 1, 1, 128}, DataType::Float32);
-    auto input_q_d = gCuda->addTensor({1, 1, 1, 64}, DataType::Float32);
+    auto input_q_d = gCuda->addTensor({1, 1, 1, 128}, DataType::Float32);
-    auto input_k_d = gCuda->addTensor({1, 1, 1, 64}, DataType::Float32);
+    auto input_k_d = gCuda->addTensor({1, 1, 1, 128}, DataType::Float32);
-    auto input_v_d = gCuda->addTensor({1, 1, 1, 64}, DataType::Float32);
+    auto input_v_d = gCuda->addTensor({1, 1, 1, 128}, DataType::Float32);
     auto position_id_d = gCuda->addTensor({1, 1}, DataType::UInt32);
 
     auto op = gCuda->addOp<AttentionKVCacheObj>(
@@ -32,11 +32,14 @@ TEST(AttentionKVCache, Cuda) {
     position_id_d->setData(IncrementalGenerator());
     cudaRuntime->run(gCuda);
 
-    auto oCpu = gCpu->cloneTensor(op->getOutput());
+    auto oCpu = gCpu->cloneTensor(op->getOutputs()[0]);
     EXPECT_TRUE(oCpu->equalData(vector<float>{
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}));
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}));
 }
 
 } // namespace infini